Self-Healing Parsers

Org Status: 🟡 Dormant Cloudflare: N/A Last Audited: 2026-04-28

When an upstream API silently moves a field from user_results.result.legacy.screen_name to user_results.result.core.screen_name, your parser should fix itself — not page you at 3am.

This article describes a five-layer pattern for building parsers that automatically detect when an API schema changes and repair themselves without downtime, manual intervention, or code deploys. The pattern emerged from production experience intercepting X/Twitter’s internal GraphQL API, where schema changes happen without warning, without versioning, and without changelog entries.

What you’ll learn:

• Why traditional parsing approaches (hardcoded paths, Zod schemas, LLM extraction) each fail in specific ways against unstable APIs
• A five-layer architecture that combines versioned schema maps, path resolution with fallback, drift detection, deterministic repair, and AI-assisted recovery
• Complete TypeScript implementations for each layer, using X/Twitter’s GraphQL response structure as a real case study
• How to store schema configurations as data (not code) in Cloudflare R2 for zero-deploy updates
• When to use this pattern vs. simpler alternatives, and what it costs in complexity

• The Problem
• Origin Story: The Silent Breakage
• Core Concepts
• The Five Layers
  • Layer 1: Versioned Schema Map
  • Layer 2: Path Resolution with Fallback
  • Layer 3: Drift Detection
  • Layer 4: Deterministic Path Repair
  • Layer 5: AI Fallback
• Putting It All Together
• Small Examples
• Comparisons
• Anti-Patterns
• Where This Pattern Applies
• Cost Analysis
• References

Every developer who consumes an API they don’t control will eventually face the same failure mode: the upstream schema changes and their parser silently returns garbage.

The failure is “silent” because most parsers are written as a chain of property accesses. When response.data.user.legacy.screen_name becomes response.data.user.core.screen_name, the old path doesn’t throw — it returns undefined. That undefined flows downstream, gets coerced to an empty string or null, and your system keeps running. It keeps running with broken data.

Here’s what the landscape of existing approaches looks like:

Hardcoded Path Access

// This is what most code looks like
const screenName = tweet.core.user_results.result.legacy.screen_name;

When the path changes, this returns undefined. If you’re lucky and the intermediate object doesn’t exist, you get a TypeError: Cannot read properties of undefined. If you’re unlucky, every intermediate object exists but the leaf moved — and you get silent undefined flowing through your entire pipeline.

Optional Chaining (the false safety net)

// "Safe" version -- still silently broken
const screenName = tweet?.core?.user_results?.result?.legacy?.screen_name ?? "unknown";

This is worse than the hardcoded version. It never throws. It never tells you something is wrong. It just returns "unknown" for every single user, and your downstream systems happily process data with every author labeled “unknown”. You discover this days later when someone notices the analytics look weird.

Schema Validation (Zod, JSON Schema)

const TweetSchema = z.object({
  core: z.object({
    user_results: z.object({
      result: z.object({
        legacy: z.object({
          screen_name: z.string(),
        }),
      }),
    }),
  }),
});

const parsed = TweetSchema.safeParse(tweet);
if (!parsed.success) {
  // Now what? You know it's broken, but you can't fix it.
  // You log an error, drop the data, and page someone.
  console.error("Schema validation failed", parsed.error);
}

Schema validation is a genuine improvement — at least you know the data doesn’t match your expectations. But “knowing it’s broken” isn’t “fixing it.” You still drop every incoming record until a human writes a new schema and deploys it. During a batch import of 500 bookmarks, that means losing all 500.

LLM-Only Parsing

const result = await llm.chat({
  messages: [{
    role: "user",
    content: `Extract the author's screen name from this JSON:\n${JSON.stringify(tweet)}`,
  }],
});

This actually works — LLMs are remarkably good at understanding JSON structure. But it costs money on every single parse ($0.01-0.10 per record depending on payload size and model), it’s slow (500ms-2s per call), and it’s non-deterministic. You might get "elonmusk" or "@elonmusk" or "Elon Musk" depending on the model’s mood. For a batch of 500 tweets, you’re looking at $5-50 and 4-16 minutes of wall time.

Key insight: Each approach solves one problem while creating another. Hardcoded paths are fast but fragile. Validation detects breakage but can’t fix it. LLMs can fix anything but are slow and expensive. The self-healing parser combines all three into a layered system where each layer’s weakness is covered by the next.

What changes if you get this right

When your parser can heal itself:

• Zero-downtime schema migrations — the API changes, your parser adapts, data keeps flowing
• No on-call pages for field moves — the most common API change (moving a field) is handled automatically
• Full audit trail — every schema version is stored immutably, you can see exactly what changed and when
• Cost stays near zero — the fast path (schema map lookup) handles 99%+ of requests, expensive operations only trigger on actual drift

We built a Chrome extension that intercepts X/Twitter’s internal GraphQL responses — HomeTimeline, Bookmarks, UserTweets, ListLatestTweetsTimeline — and forwards them to a backend for analysis. The extension uses a MAIN world content script to override window.fetch, capture GraphQL responses as they stream back from Twitter’s servers, and forward them to our API.

The GraphQL responses from X/Twitter are deeply nested. Here’s a simplified version of the real structure:

// Simplified X/Twitter HomeTimeline GraphQL response
// Real responses are 50-200KB per request
{
  data: {
    home: {
      home_timeline_urt: {
        instructions: [{
          type: "TimelineAddEntries",
          entries: [{
            content: {
              entryType: "TimelineTimelineItem",
              itemContent: {
                tweet_results: {
                  result: {
                    __typename: "Tweet",
                    rest_id: "1234567890",
                    core: {
                      user_results: {
                        result: {
                          __typename: "User",
                          id: "VXNlcjoxMjM0NTY3ODkw",
                          rest_id: "1234567890",
                          legacy: {
                            screen_name: "elonmusk",   // <-- HERE
                            name: "Elon Musk",
                            followers_count: 180000000,
                            verified: false,
                          },
                          is_blue_verified: true,
                        }
                      }
                    },
                    legacy: {
                      full_text: "The tweet content...",
                      created_at: "Sat Mar 01 12:00:00 +0000 2025",
                      favorite_count: 50000,
                      retweet_count: 10000,
                    }
                  }
                }
              }
            }
          }]
        }]
      }
    }
  }
}

The path to screen_name is:

data.home.home_timeline_urt.instructions[0].entries[0].content.itemContent
  .tweet_results.result.core.user_results.result.legacy.screen_name

That’s thirteen levels deep.

One day, X moved screen_name from inside legacy to inside core for certain response types. No announcement. No deprecation period. No versioning header. The legacy object still existed — it just no longer contained screen_name in some contexts.

Our parser silently returned empty strings for author handles. We had screen_name: undefined coerced to "" for thousands of bookmarks. The data was technically “parsed” — every other field was fine. But every tweet was attributed to nobody.

We discovered the breakage 48 hours later when a downstream report showed 0 unique authors across 2,000 tweets. Two days of data, unrecoverable (the raw responses weren’t stored — only the parsed output).

That’s when we built the self-healing parser.

Concept 1: Schema Map (data, not code)

A schema map is a JSON document that describes the dot-paths to every field you care about in an API response. It’s stored externally (we use Cloudflare R2), versioned immutably, and loaded at runtime.

interface SchemaMap {
  /** Monotonically increasing version number */
  version: number;

  /** ISO 8601 timestamp of creation */
  createdAt: string;

  /** Who created this version */
  createdBy: "hardcoded" | "deterministic-repair" | "ai-repair" | "manual";

  /** Human-readable description of what changed */
  changelog: string;

  /** Paths to user-related fields within a tweet result object */
  paths: {
    /** Path from tweet root to the user result object */
    userResult: string;

    /** Candidate paths for screen_name (tried in order) */
    screenName: string[];

    /** Candidate paths for display name */
    displayName: string[];

    /** Candidate paths for follower count */
    followersCount: string[];

    /** Candidate paths for verified status */
    verified: string[];

    /** Candidate paths for profile image URL */
    profileImageUrl: string[];

    /** Path from tweet root to the tweet text */
    tweetText: string;

    /** Path from tweet root to created_at */
    tweetCreatedAt: string;

    /** Candidate paths for media entities */
    mediaEntities: string[];
  };
}

Key insight: The schema map is the central innovation. By extracting paths into data, you decouple “where the field is” from “how to use the field.” Updating a path requires changing a JSON file, not a code deploy. And because each version is immutable, you have a complete history of every schema change.

Here’s what a real schema map looks like:

const schemaMapV1: SchemaMap = {
  version: 1,
  createdAt: "2025-01-15T00:00:00Z",
  createdBy: "hardcoded",
  changelog: "Initial schema map based on HomeTimeline structure as of 2025-01-15",
  paths: {
    userResult: "core.user_results.result",
    screenName: ["legacy.screen_name"],
    displayName: ["legacy.name"],
    followersCount: ["legacy.followers_count"],
    verified: ["is_blue_verified", "legacy.verified"],
    profileImageUrl: ["legacy.profile_image_url_https"],
    tweetText: "legacy.full_text",
    tweetCreatedAt: "legacy.created_at",
    mediaEntities: [
      "legacy.extended_entities.media",
      "legacy.entities.media",
    ],
  },
};

And here’s what it looks like after a repair:

const schemaMapV2: SchemaMap = {
  version: 2,
  createdAt: "2025-03-22T14:30:00Z",
  createdBy: "deterministic-repair",
  changelog: "screen_name moved from legacy to core; added core.screen_name as primary path",
  paths: {
    userResult: "core.user_results.result",
    screenName: ["core.screen_name", "legacy.screen_name"],  // NEW path first
    displayName: ["legacy.name"],
    followersCount: ["legacy.followers_count"],
    verified: ["is_blue_verified", "legacy.verified"],
    profileImageUrl: ["legacy.profile_image_url_https"],
    tweetText: "legacy.full_text",
    tweetCreatedAt: "legacy.created_at",
    mediaEntities: [
      "legacy.extended_entities.media",
      "legacy.entities.media",
    ],
  },
};

Notice: the old path (legacy.screen_name) is kept as a fallback. Schema maps are additive — new paths go to the front, old paths stay as fallbacks.

Concept 2: Path Resolution

Path resolution is the process of extracting a value from a deeply nested object using a dot-path string. It’s the runtime counterpart to the schema map.

/**
 * Walk a dot-path like "core.user_results.result.legacy.screen_name"
 * against a nested object. Returns undefined if any segment is missing.
 */
function walkPath(obj: unknown, path: string): unknown {
  const segments = path.split(".");
  let current: unknown = obj;

  for (const segment of segments) {
    if (current === null || current === undefined) return undefined;
    if (typeof current !== "object") return undefined;
    current = (current as Record<string, unknown>)[segment];
  }

  return current;
}

// Usage
const user = walkPath(tweetResult, "core.user_results.result");
// user is now the User object (or undefined if the path is wrong)

const screenName = walkPath(user, "legacy.screen_name");
// screenName is now "elonmusk" (or undefined if moved)

Key insight: walkPath is O(d) where d is the depth of the path (typically 3-6 segments). It never searches — it follows a known route. This is your fast path, and it handles 99%+ of requests.

Concept 3: Deep Find (the rescue operation)

Deep find is a BFS traversal that searches an entire object tree for a key, returning the first value found. It’s the rescue operation that keeps data flowing when the schema map’s paths are stale.

/**
 * BFS search for a key in a nested object.
 * Returns the first value found at the shallowest depth.
 *
 * @param obj - The object to search
 * @param targetKey - The key to find (e.g., "screen_name")
 * @param maxDepth - Maximum depth to search (prevents runaway on huge objects)
 */
function deepFind(
  obj: unknown,
  targetKey: string,
  maxDepth: number = 6
): unknown {
  if (obj === null || obj === undefined || typeof obj !== "object") {
    return undefined;
  }

  // BFS queue: [object, currentDepth]
  const queue: Array<[Record<string, unknown>, number]> = [
    [obj as Record<string, unknown>, 0],
  ];

  while (queue.length > 0) {
    const [current, depth] = queue.shift()!;

    if (depth > maxDepth) continue;

    for (const key of Object.keys(current)) {
      if (key === targetKey) {
        return current[key];
      }

      const value = current[key];
      if (value !== null && typeof value === "object" && !Array.isArray(value)) {
        queue.push([value as Record<string, unknown>, depth + 1]);
      }
    }
  }

  return undefined;
}

Key insight: BFS finds the shallowest match, which is almost always the correct one. If screen_name exists at depth 2 in core and at depth 4 in some nested retweet object, BFS returns the shallow one. DFS would find whichever branch it explored first — which might be the wrong one.

Concept 4: Drift Detection

Drift detection is a statistical check that runs after parsing a batch of records. If any critical field is missing from any record in the batch, we know the schema has drifted.

interface DriftReport {
  /** Total records in the batch */
  total: number;

  /** Records where all critical fields were found via schema paths */
  cleanHits: number;

  /** Records where at least one field required deepFind */
  deepFindRescues: number;

  /** Records where a critical field could not be found at all */
  failures: number;

  /** Which fields triggered deepFind, and how many times */
  driftedFields: Record<string, number>;

  /** Overall drift status */
  status: "HEALTHY" | "SCHEMA_DRIFT" | "SCHEMA_BROKEN";
}

Key insight: Drift detection is not about individual records — it’s about batches. A single missing field could be a malformed record. When 50% of records in a batch are missing screen_name, that’s a schema change.

Concept 5: Path Repair

Path repair is the process of discovering where a field actually lives in the current API response, then updating the schema map so future requests use the new path directly. There are two repair strategies: deterministic (BFS-based, free, fast) and AI-assisted (LLM-based, costs money, handles structural changes).

interface RepairResult {
  /** The field that was repaired */
  field: string;

  /** The old path(s) that stopped working */
  oldPaths: string[];

  /** The new path discovered by repair */
  newPath: string;

  /** How the new path was found */
  method: "deterministic-bfs" | "ai-inference";

  /** Confidence level (1.0 for deterministic, varies for AI) */
  confidence: number;

  /** The raw JSON sample used for repair (for audit) */
  samplePayload?: unknown;
}

Layer 1: Versioned Schema Map

The schema map lives in external storage — not in your application code. We use Cloudflare R2 because it’s fast, cheap, and our Workers can access it with zero latency via bindings. You could use S3, GCS, a KV store, or even a database.

Storage Layout

r2://schema-maps/
  twitter/
    HomeTimeline/
      v1.json        # Original schema
      v2.json        # After screen_name moved
      v3.json        # After media entities restructured
      latest.json    # Always points to the current version
    Bookmarks/
      v1.json
      latest.json
    UserTweets/
      v1.json
      latest.json

Schema Map Manager

interface SchemaMapStore {
  /** Load the current schema map for an endpoint */
  getLatest(endpoint: string): Promise<SchemaMap>;

  /** Load a specific version */
  getVersion(endpoint: string, version: number): Promise<SchemaMap>;

  /** Store a new schema map version */
  putVersion(endpoint: string, map: SchemaMap): Promise<void>;

  /** Update latest to point to a specific version */
  setLatest(endpoint: string, version: number): Promise<void>;
}

class R2SchemaMapStore implements SchemaMapStore {
  constructor(private bucket: R2Bucket) {}

  async getLatest(endpoint: string): Promise<SchemaMap> {
    const key = `twitter/${endpoint}/latest.json`;
    const obj = await this.bucket.get(key);
    if (!obj) throw new Error(`No schema map found for ${endpoint}`);
    return obj.json<SchemaMap>();
  }

  async getVersion(endpoint: string, version: number): Promise<SchemaMap> {
    const key = `twitter/${endpoint}/v${version}.json`;
    const obj = await this.bucket.get(key);
    if (!obj) throw new Error(`Schema map v${version} not found for ${endpoint}`);
    return obj.json<SchemaMap>();
  }

  async putVersion(endpoint: string, map: SchemaMap): Promise<void> {
    const key = `twitter/${endpoint}/v${map.version}.json`;
    await this.bucket.put(key, JSON.stringify(map, null, 2), {
      httpMetadata: { contentType: "application/json" },
      customMetadata: {
        createdBy: map.createdBy,
        changelog: map.changelog,
      },
    });
  }

  async setLatest(endpoint: string, version: number): Promise<void> {
    // Load the version first to verify it exists
    const map = await this.getVersion(endpoint, version);
    const key = `twitter/${endpoint}/latest.json`;
    await this.bucket.put(key, JSON.stringify(map, null, 2), {
      httpMetadata: { contentType: "application/json" },
    });
  }
}

In-Memory Cache with TTL

You don’t want to hit R2 on every request. Cache the schema map in memory with a short TTL.

class CachedSchemaMapStore implements SchemaMapStore {
  private cache = new Map<string, { map: SchemaMap; expiresAt: number }>();
  private ttlMs: number;

  constructor(
    private inner: SchemaMapStore,
    ttlMs: number = 60_000, // 1 minute default
  ) {
    this.ttlMs = ttlMs;
  }

  async getLatest(endpoint: string): Promise<SchemaMap> {
    const cacheKey = `latest:${endpoint}`;
    const cached = this.cache.get(cacheKey);

    if (cached && cached.expiresAt > Date.now()) {
      return cached.map;
    }

    const map = await this.inner.getLatest(endpoint);
    this.cache.set(cacheKey, {
      map,
      expiresAt: Date.now() + this.ttlMs,
    });
    return map;
  }

  /** Force-invalidate after a repair writes a new version */
  invalidate(endpoint: string): void {
    this.cache.delete(`latest:${endpoint}`);
  }

  // getVersion, putVersion, setLatest delegate to inner
  async getVersion(endpoint: string, version: number): Promise<SchemaMap> {
    return this.inner.getVersion(endpoint, version);
  }

  async putVersion(endpoint: string, map: SchemaMap): Promise<void> {
    await this.inner.putVersion(endpoint, map);
  }

  async setLatest(endpoint: string, version: number): Promise<void> {
    await this.inner.setLatest(endpoint, version);
    this.invalidate(endpoint);
  }
}

Key insight: The schema map is immutable once written. v1.json never changes. When repair discovers a new path, it creates v2.json and updates latest.json to point to it. This gives you a complete audit trail and instant rollback — just point latest.json back to the previous version.

Layer 2: Path Resolution with Fallback

This is the core parsing logic. For every field, try the schema map’s paths first (fast), then fall back to deep find (expensive but self-healing).

interface ResolvedField<T = unknown> {
  /** The extracted value */
  value: T | undefined;

  /** Which path succeeded (or "deepFind" if rescue was needed) */
  resolvedVia: string | "deepFind";

  /** Whether the fast path worked */
  usedDeepFind: boolean;
}

/**
 * Try each candidate path in order. If all fail, fall back to deepFind.
 *
 * @param obj - The object to search within
 * @param paths - Ordered list of dot-paths to try (from schema map)
 * @param deepFindKey - The key name to search for if all paths fail
 * @param maxDepth - Maximum depth for deepFind traversal
 */
function resolveField<T = unknown>(
  obj: unknown,
  paths: string[],
  deepFindKey: string,
  maxDepth: number = 6,
): ResolvedField<T> {
  // Fast path: try each schema map path in order
  for (const path of paths) {
    const value = walkPath(obj, path);
    if (value !== undefined && value !== null) {
      return {
        value: value as T,
        resolvedVia: path,
        usedDeepFind: false,
      };
    }
  }

  // Rescue path: BFS search for the key
  const value = deepFind(obj, deepFindKey, maxDepth);
  return {
    value: value as T | undefined,
    resolvedVia: "deepFind",
    usedDeepFind: true,
  };
}

The Full Tweet Parser

interface ParsedTweet {
  tweetId: string;
  text: string;
  createdAt: string;
  author: {
    userId: string;
    screenName: string;
    displayName: string;
    followersCount: number;
    verified: boolean;
    profileImageUrl: string;
  };
  metrics: {
    favoriteCount: number;
    retweetCount: number;
    replyCount: number;
    viewCount: number;
  };
}

interface ParseResult {
  tweet: ParsedTweet | null;
  driftSignals: string[];
}

function parseTweetResult(
  tweetResult: Record<string, unknown>,
  schemaMap: SchemaMap,
): ParseResult {
  const driftSignals: string[] = [];

  // Resolve user object first
  const userObj = walkPath(tweetResult, schemaMap.paths.userResult);
  if (!userObj || typeof userObj !== "object") {
    // If we can't find the user object at all, try deepFind for user_results
    const rescued = deepFind(tweetResult, "user_results", 4);
    if (!rescued) {
      return { tweet: null, driftSignals: ["userResult:missing"] };
    }
    driftSignals.push("userResult:deepFind");
  }

  const user = (userObj ?? deepFind(tweetResult, "user_results", 4)) as
    | Record<string, unknown>
    | undefined;

  if (!user) {
    return { tweet: null, driftSignals: ["userResult:missing"] };
  }

  // Resolve each user field
  const screenName = resolveField<string>(
    user,
    schemaMap.paths.screenName,
    "screen_name",
  );
  if (screenName.usedDeepFind) driftSignals.push("screenName:deepFind");

  const displayName = resolveField<string>(
    user,
    schemaMap.paths.displayName,
    "name",
  );
  if (displayName.usedDeepFind) driftSignals.push("displayName:deepFind");

  const followersCount = resolveField<number>(
    user,
    schemaMap.paths.followersCount,
    "followers_count",
  );
  if (followersCount.usedDeepFind) driftSignals.push("followersCount:deepFind");

  const verified = resolveField<boolean>(
    user,
    schemaMap.paths.verified,
    "is_blue_verified",
  );
  if (verified.usedDeepFind) driftSignals.push("verified:deepFind");

  const profileImageUrl = resolveField<string>(
    user,
    schemaMap.paths.profileImageUrl,
    "profile_image_url_https",
  );
  if (profileImageUrl.usedDeepFind) driftSignals.push("profileImageUrl:deepFind");

  // Resolve tweet fields
  const text = walkPath(tweetResult, schemaMap.paths.tweetText) as
    | string
    | undefined;
  const createdAt = walkPath(tweetResult, schemaMap.paths.tweetCreatedAt) as
    | string
    | undefined;

  if (!text) driftSignals.push("tweetText:missing");
  if (!createdAt) driftSignals.push("tweetCreatedAt:missing");

  // Build parsed tweet
  const tweet: ParsedTweet = {
    tweetId: (tweetResult as any).rest_id ?? "",
    text: text ?? "",
    createdAt: createdAt ?? "",
    author: {
      userId: (user as any).rest_id ?? "",
      screenName: screenName.value ?? "",
      displayName: displayName.value ?? "",
      followersCount: followersCount.value ?? 0,
      verified: verified.value ?? false,
      profileImageUrl: profileImageUrl.value ?? "",
    },
    metrics: {
      favoriteCount:
        (walkPath(tweetResult, "legacy.favorite_count") as number) ?? 0,
      retweetCount:
        (walkPath(tweetResult, "legacy.retweet_count") as number) ?? 0,
      replyCount:
        (walkPath(tweetResult, "legacy.reply_count") as number) ?? 0,
      viewCount: Number(
        walkPath(tweetResult, "views.count") ?? 0,
      ),
    },
  };

  return { tweet, driftSignals };
}

Key insight: Every field resolution returns metadata about how it was resolved. The driftSignals array is the input to drift detection. If we parsed 100 tweets and 80 of them have "screenName:deepFind", that tells us the screenName paths in the schema map are stale.

Layer 3: Drift Detection

After parsing a batch, we aggregate the drift signals from every record into a drift report. This report drives the repair decision.

interface DriftReport {
  total: number;
  cleanHits: number;
  deepFindRescues: number;
  failures: number;
  driftedFields: Record<string, number>;
  status: "HEALTHY" | "SCHEMA_DRIFT" | "SCHEMA_BROKEN";
  timestamp: string;
  schemaVersion: number;
}

function buildDriftReport(
  results: ParseResult[],
  schemaVersion: number,
): DriftReport {
  const report: DriftReport = {
    total: results.length,
    cleanHits: 0,
    deepFindRescues: 0,
    failures: 0,
    driftedFields: {},
    status: "HEALTHY",
    timestamp: new Date().toISOString(),
    schemaVersion,
  };

  for (const result of results) {
    if (result.tweet === null) {
      report.failures++;
      continue;
    }

    if (result.driftSignals.length === 0) {
      report.cleanHits++;
    } else {
      report.deepFindRescues++;

      for (const signal of result.driftSignals) {
        const field = signal.split(":")[0];
        report.driftedFields[field] = (report.driftedFields[field] ?? 0) + 1;
      }
    }
  }

  // Determine status
  if (report.failures > 0) {
    report.status = "SCHEMA_BROKEN";
  } else if (report.deepFindRescues > 0) {
    report.status = "SCHEMA_DRIFT";
  } else {
    report.status = "HEALTHY";
  }

  return report;
}

Thresholds and Decision Logic

interface DriftConfig {
  /** Trigger repair if this percentage of records required deepFind */
  driftThreshold: number; // e.g., 0.0 = any drift triggers repair

  /** Trigger alert if this percentage of records completely failed */
  failureThreshold: number; // e.g., 0.5 = 50% failure rate

  /** Minimum batch size before drift detection is meaningful */
  minBatchSize: number; // e.g., 5

  /** Cooldown between repair attempts (prevents storms) */
  repairCooldownMs: number; // e.g., 600_000 = 10 minutes
}

const DEFAULT_DRIFT_CONFIG: DriftConfig = {
  driftThreshold: 0.0,     // Any drift triggers repair
  failureThreshold: 0.5,   // 50% failure rate triggers alert
  minBatchSize: 5,         // Need at least 5 records
  repairCooldownMs: 600_000, // 10 minute cooldown
};

interface DriftDecision {
  shouldRepair: boolean;
  shouldAlert: boolean;
  reason: string;
}

function decideDriftAction(
  report: DriftReport,
  config: DriftConfig,
  lastRepairTimestamp: number | null,
): DriftDecision {
  // Not enough data
  if (report.total < config.minBatchSize) {
    return {
      shouldRepair: false,
      shouldAlert: false,
      reason: `Batch too small (${report.total} < ${config.minBatchSize})`,
    };
  }

  // Check cooldown
  const now = Date.now();
  if (lastRepairTimestamp && now - lastRepairTimestamp < config.repairCooldownMs) {
    const remainingMs = config.repairCooldownMs - (now - lastRepairTimestamp);
    return {
      shouldRepair: false,
      shouldAlert: report.status === "SCHEMA_BROKEN",
      reason: `Repair cooldown active (${Math.ceil(remainingMs / 1000)}s remaining)`,
    };
  }

  // Check failure rate
  const failureRate = report.failures / report.total;
  if (failureRate > config.failureThreshold) {
    return {
      shouldRepair: true,
      shouldAlert: true,
      reason: `High failure rate: ${(failureRate * 100).toFixed(1)}% of records failed to parse`,
    };
  }

  // Check drift rate
  const driftRate = report.deepFindRescues / report.total;
  if (driftRate > config.driftThreshold) {
    return {
      shouldRepair: true,
      shouldAlert: false,
      reason: `Schema drift detected: ${report.deepFindRescues}/${report.total} records required deepFind`,
    };
  }

  return {
    shouldRepair: false,
    shouldAlert: false,
    reason: "All records parsed via schema map paths",
  };
}

Key insight: The threshold of 0.0 for drift means “repair on any deepFind usage.” This is aggressive but correct — if even one record needs deepFind, the schema map is stale. You want to repair proactively, not wait until 50% of records are breaking. The 10-minute cooldown prevents repair storms when the API is actively being deployed.

Layer 4: Deterministic Path Repair

When drift is detected, the first repair strategy is deterministic: use BFS to find where each drifted field actually lives in a sample payload, then update the schema map with the new path.

/**
 * BFS through an object to find the full dot-path to a target key.
 * Returns the shallowest path found.
 *
 * @param obj - The root object to search
 * @param targetKey - The key to find (e.g., "screen_name")
 * @param maxDepth - Maximum depth to search
 * @returns The dot-path (e.g., "core.screen_name") or null if not found
 */
function findPath(
  obj: unknown,
  targetKey: string,
  maxDepth: number = 6,
): string | null {
  if (obj === null || obj === undefined || typeof obj !== "object") {
    return null;
  }

  // BFS queue: [object, currentPath, currentDepth]
  const queue: Array<[Record<string, unknown>, string, number]> = [
    [obj as Record<string, unknown>, "", 0],
  ];

  while (queue.length > 0) {
    const [current, currentPath, depth] = queue.shift()!;

    if (depth > maxDepth) continue;

    for (const key of Object.keys(current)) {
      const fullPath = currentPath ? `${currentPath}.${key}` : key;

      if (key === targetKey) {
        return fullPath;
      }

      const value = current[key];
      if (value !== null && typeof value === "object" && !Array.isArray(value)) {
        queue.push([value as Record<string, unknown>, fullPath, depth + 1]);
      }
    }
  }

  return null;
}

Finding All Occurrences

Sometimes a key appears in multiple places (e.g., the tweet author’s screen_name and a retweeted author’s screen_name). We need to find all paths and pick the right one.

/**
 * Find ALL paths to a target key, sorted by depth (shallowest first).
 */
function findAllPaths(
  obj: unknown,
  targetKey: string,
  maxDepth: number = 8,
): string[] {
  const results: Array<{ path: string; depth: number }> = [];

  if (obj === null || obj === undefined || typeof obj !== "object") {
    return [];
  }

  const queue: Array<[Record<string, unknown>, string, number]> = [
    [obj as Record<string, unknown>, "", 0],
  ];

  while (queue.length > 0) {
    const [current, currentPath, depth] = queue.shift()!;

    if (depth > maxDepth) continue;

    for (const key of Object.keys(current)) {
      const fullPath = currentPath ? `${currentPath}.${key}` : key;

      if (key === targetKey) {
        results.push({ path: fullPath, depth });
      }

      const value = current[key];
      if (value !== null && typeof value === "object" && !Array.isArray(value)) {
        queue.push([value as Record<string, unknown>, fullPath, depth + 1]);
      }
    }
  }

  // Sort by depth (shallowest first)
  results.sort((a, b) => a.depth - b.depth);
  return results.map((r) => r.path);
}

The Repair Engine

// Map from our field names to the key we search for in the raw JSON
const FIELD_KEY_MAP: Record<string, string> = {
  screenName: "screen_name",
  displayName: "name",
  followersCount: "followers_count",
  verified: "is_blue_verified",
  profileImageUrl: "profile_image_url_https",
};

interface RepairPlan {
  /** Fields that were successfully repaired */
  repairs: RepairResult[];

  /** Fields that could not be repaired deterministically */
  unrepairable: string[];

  /** The new schema map (if any repairs were made) */
  newSchemaMap: SchemaMap | null;
}

async function deterministicRepair(
  driftReport: DriftReport,
  currentMap: SchemaMap,
  samplePayloads: Record<string, unknown>[],
): Promise<RepairPlan> {
  const repairs: RepairResult[] = [];
  const unrepairable: string[] = [];

  // For each drifted field, find its new location
  for (const field of Object.keys(driftReport.driftedFields)) {
    const searchKey = FIELD_KEY_MAP[field];
    if (!searchKey) {
      unrepairable.push(field);
      continue;
    }

    // Search across multiple sample payloads for consensus
    const pathCandidates = new Map<string, number>();

    for (const payload of samplePayloads) {
      // Navigate to user object first (since our paths are relative to user)
      const userObj = walkPath(payload, currentMap.paths.userResult) ??
        deepFind(payload, "user_results", 4);

      if (!userObj || typeof userObj !== "object") continue;

      const paths = findAllPaths(userObj, searchKey, 6);
      for (const path of paths) {
        pathCandidates.set(path, (pathCandidates.get(path) ?? 0) + 1);
      }
    }

    if (pathCandidates.size === 0) {
      unrepairable.push(field);
      continue;
    }

    // Pick the path that appears most frequently (consensus across samples)
    const bestPath = [...pathCandidates.entries()]
      .sort((a, b) => b[1] - a[1])[0][0];

    const oldPaths = currentMap.paths[field as keyof typeof currentMap.paths];

    repairs.push({
      field,
      oldPaths: Array.isArray(oldPaths) ? oldPaths : [String(oldPaths)],
      newPath: bestPath,
      method: "deterministic-bfs",
      confidence: 1.0,
    });
  }

  // Build new schema map if we found any repairs
  if (repairs.length === 0) {
    return { repairs, unrepairable, newSchemaMap: null };
  }

  const newPaths = { ...currentMap.paths };

  for (const repair of repairs) {
    const currentPaths = newPaths[repair.field as keyof typeof newPaths];
    if (Array.isArray(currentPaths)) {
      // Prepend new path, keep old paths as fallbacks
      (newPaths as any)[repair.field] = [
        repair.newPath,
        ...currentPaths.filter((p: string) => p !== repair.newPath),
      ];
    } else {
      // Single path field -- replace
      (newPaths as any)[repair.field] = repair.newPath;
    }
  }

  const changelog = repairs
    .map((r) => `${r.field}: ${r.oldPaths[0]} -> ${r.newPath}`)
    .join("; ");

  const newSchemaMap: SchemaMap = {
    version: currentMap.version + 1,
    createdAt: new Date().toISOString(),
    createdBy: "deterministic-repair",
    changelog,
    paths: newPaths,
  };

  return { repairs, unrepairable, newSchemaMap };
}

Key insight: Deterministic repair handles 99% of real-world API changes. Fields don’t disappear — they move. BFS finds where they went. The consensus mechanism (checking multiple sample payloads) prevents false repairs from one-off malformed records. And the cost is zero — it’s just object traversal.

Layer 5: AI Fallback

When deterministic repair fails — the field name itself changed, or the data structure was reorganized beyond a simple move — we fall back to an LLM. This is the last resort, not the first.

interface AIRepairRequest {
  /** The field we're trying to find */
  field: string;

  /** What we expect the field to contain (for context) */
  fieldDescription: string;

  /** Example values from previous working schema */
  exampleValues: string[];

  /** A sample payload where the field could not be found */
  samplePayload: Record<string, unknown>;

  /** The current schema map (for context) */
  currentSchemaMap: SchemaMap;
}

interface AIRepairResponse {
  /** The proposed dot-path to the field */
  proposedPath: string;

  /** The value found at that path (for verification) */
  extractedValue: unknown;

  /** The model's reasoning */
  reasoning: string;

  /** Confidence score (0-1) */
  confidence: number;
}

async function aiRepair(
  request: AIRepairRequest,
  options: {
    model?: string;
    apiKey: string;
    baseUrl?: string;
    maxTokens?: number;
  },
): Promise<AIRepairResponse | null> {
  // Truncate the payload to avoid blowing up token usage
  const truncatedPayload = truncateForLLM(request.samplePayload, 4000);

  const systemPrompt = `You are a JSON schema repair assistant. Your job is to find where a specific field has moved in a JSON structure.

You will be given:
1. A field name and description
2. Example values this field typically contains
3. A JSON payload where the field cannot be found at its expected location
4. The current schema map showing where the field used to be

Your task: Find the new dot-path to this field in the JSON payload.

Respond ONLY with valid JSON matching this schema:
{
  "proposedPath": "the.dot.path.to.the.field",
  "extractedValue": "the value found at that path",
  "reasoning": "brief explanation of what changed",
  "confidence": 0.95
}`;

  const userPrompt = `Find the new location of the "${request.field}" field.

Description: ${request.fieldDescription}
Example values: ${JSON.stringify(request.exampleValues)}

Previous paths (no longer working):
${JSON.stringify(request.currentSchemaMap.paths[request.field as keyof typeof request.currentSchemaMap.paths])}

JSON payload (truncated):
${JSON.stringify(truncatedPayload, null, 2)}`;

  try {
    const response = await fetch(
      options.baseUrl ?? "https://api.openai.com/v1/chat/completions",
      {
        method: "POST",
        headers: {
          "Content-Type": "application/json",
          Authorization: `Bearer ${options.apiKey}`,
        },
        body: JSON.stringify({
          model: options.model ?? "gpt-4o-mini",
          messages: [
            { role: "system", content: systemPrompt },
            { role: "user", content: userPrompt },
          ],
          max_tokens: options.maxTokens ?? 500,
          temperature: 0,
          response_format: { type: "json_object" },
        }),
      },
    );

    if (!response.ok) {
      console.error(`AI repair failed: ${response.status} ${response.statusText}`);
      return null;
    }

    const data = await response.json() as any;
    const content = data.choices?.[0]?.message?.content;
    if (!content) return null;

    const parsed = JSON.parse(content) as AIRepairResponse;

    // Verify the proposed path actually works
    const verification = walkPath(request.samplePayload, parsed.proposedPath);
    if (verification === undefined) {
      console.warn(
        `AI proposed path "${parsed.proposedPath}" but it doesn't resolve to a value`,
      );
      return null;
    }

    return {
      ...parsed,
      extractedValue: verification,
    };
  } catch (err) {
    console.error("AI repair error:", err);
    return null;
  }
}

/**
 * Truncate a JSON object to approximately maxChars characters
 * by removing deeply nested content.
 */
function truncateForLLM(
  obj: unknown,
  maxChars: number,
  currentDepth: number = 0,
  maxDepth: number = 5,
): unknown {
  if (currentDepth >= maxDepth) return "[truncated]";

  if (Array.isArray(obj)) {
    // Keep only first 2 items of arrays
    return obj.slice(0, 2).map((item) =>
      truncateForLLM(item, maxChars, currentDepth + 1, maxDepth)
    );
  }

  if (obj !== null && typeof obj === "object") {
    const result: Record<string, unknown> = {};
    for (const [key, value] of Object.entries(obj as Record<string, unknown>)) {
      result[key] = truncateForLLM(value, maxChars, currentDepth + 1, maxDepth);
    }
    return result;
  }

  // Truncate long strings
  if (typeof obj === "string" && obj.length > 200) {
    return obj.slice(0, 200) + "...";
  }

  return obj;
}

The Debounced AI Repair Controller

class AIRepairController {
  private lastAttempt: number = 0;
  private cooldownMs: number;
  private pendingRepairs = new Map<string, Promise<AIRepairResponse | null>>();

  constructor(cooldownMs: number = 600_000) {
    this.cooldownMs = cooldownMs;
  }

  async attemptRepair(
    request: AIRepairRequest,
    options: { model?: string; apiKey: string; baseUrl?: string },
  ): Promise<AIRepairResponse | null> {
    const now = Date.now();

    // Cooldown check
    if (now - this.lastAttempt < this.cooldownMs) {
      return null;
    }

    // Dedup: if we're already repairing this field, return the pending promise
    const existing = this.pendingRepairs.get(request.field);
    if (existing) return existing;

    this.lastAttempt = now;

    const promise = aiRepair(request, options).finally(() => {
      this.pendingRepairs.delete(request.field);
    });

    this.pendingRepairs.set(request.field, promise);
    return promise;
  }
}

Key insight: The AI layer uses the cheapest model that supports structured output (GPT-4o-mini at ~$0.15/1M input tokens). A single repair call costs $0.001-0.005 depending on payload size. Temperature is 0 for determinism. And we verify the proposed path actually resolves to a value before accepting it — the LLM proposes, the code verifies.

Here’s the complete orchestration that ties all five layers together:

class SelfHealingParser {
  private schemaStore: CachedSchemaMapStore;
  private aiRepairController: AIRepairController;
  private driftConfig: DriftConfig;
  private lastRepairTimestamp: number | null = null;
  private endpoint: string;

  constructor(options: {
    bucket: R2Bucket;
    endpoint: string;
    driftConfig?: Partial<DriftConfig>;
    aiRepairCooldownMs?: number;
    schemaCacheTtlMs?: number;
  }) {
    this.endpoint = options.endpoint;
    this.schemaStore = new CachedSchemaMapStore(
      new R2SchemaMapStore(options.bucket),
      options.schemaCacheTtlMs ?? 60_000,
    );
    this.aiRepairController = new AIRepairController(
      options.aiRepairCooldownMs ?? 600_000,
    );
    this.driftConfig = { ...DEFAULT_DRIFT_CONFIG, ...options.driftConfig };
  }

  /**
   * Parse a batch of tweet results from a GraphQL response.
   * Returns parsed tweets + a drift report.
   * Automatically triggers repair if drift is detected.
   */
  async parseBatch(
    tweetResults: Record<string, unknown>[],
    options?: {
      aiApiKey?: string;
      aiBaseUrl?: string;
    },
  ): Promise<{
    tweets: ParsedTweet[];
    driftReport: DriftReport;
    repairPlan: RepairPlan | null;
  }> {
    // 1. Load current schema map
    const schemaMap = await this.schemaStore.getLatest(this.endpoint);

    // 2. Parse all records
    const results: ParseResult[] = tweetResults.map((tr) =>
      parseTweetResult(tr, schemaMap)
    );

    // 3. Build drift report
    const driftReport = buildDriftReport(results, schemaMap.version);

    // 4. Decide if repair is needed
    const decision = decideDriftAction(
      driftReport,
      this.driftConfig,
      this.lastRepairTimestamp,
    );

    let repairPlan: RepairPlan | null = null;

    if (decision.shouldRepair) {
      // 5. Attempt deterministic repair first
      repairPlan = await deterministicRepair(
        driftReport,
        schemaMap,
        tweetResults.slice(0, 10), // Use up to 10 samples
      );

      // 6. If deterministic repair couldn't fix everything, try AI
      if (
        repairPlan.unrepairable.length > 0 &&
        options?.aiApiKey
      ) {
        for (const field of repairPlan.unrepairable) {
          const aiResult = await this.aiRepairController.attemptRepair(
            {
              field,
              fieldDescription: getFieldDescription(field),
              exampleValues: getExampleValues(field),
              samplePayload: tweetResults[0],
              currentSchemaMap: schemaMap,
            },
            {
              apiKey: options.aiApiKey,
              baseUrl: options.aiBaseUrl,
            },
          );

          if (aiResult && aiResult.confidence > 0.8) {
            repairPlan.repairs.push({
              field,
              oldPaths: [],
              newPath: aiResult.proposedPath,
              method: "ai-inference",
              confidence: aiResult.confidence,
              samplePayload: tweetResults[0],
            });
            repairPlan.unrepairable = repairPlan.unrepairable.filter(
              (f) => f !== field,
            );
          }
        }
      }

      // 7. If we have repairs, save the new schema map
      if (repairPlan.newSchemaMap || repairPlan.repairs.length > 0) {
        const newMap = repairPlan.newSchemaMap ?? buildNewSchemaMap(
          schemaMap,
          repairPlan.repairs,
        );

        await this.schemaStore.putVersion(this.endpoint, newMap);
        await this.schemaStore.setLatest(this.endpoint, newMap.version);

        this.lastRepairTimestamp = Date.now();

        // Log the repair for audit
        console.log(
          `[SelfHealingParser] Schema repaired: v${schemaMap.version} -> v${newMap.version}`,
          `Changes: ${newMap.changelog}`,
        );
      }
    }

    if (decision.shouldAlert) {
      console.error(
        `[SelfHealingParser] ALERT: ${decision.reason}`,
        JSON.stringify(driftReport),
      );
    }

    return {
      tweets: results
        .filter((r) => r.tweet !== null)
        .map((r) => r.tweet!),
      driftReport,
      repairPlan,
    };
  }
}

// Helper functions for AI context
function getFieldDescription(field: string): string {
  const descriptions: Record<string, string> = {
    screenName: "The user's @handle on Twitter/X (e.g., 'elonmusk')",
    displayName: "The user's display name (e.g., 'Elon Musk')",
    followersCount: "Number of followers (integer)",
    verified: "Whether the user has a blue verification badge (boolean)",
    profileImageUrl: "HTTPS URL to the user's profile image",
  };
  return descriptions[field] ?? field;
}

function getExampleValues(field: string): string[] {
  const examples: Record<string, string[]> = {
    screenName: ["elonmusk", "jack", "naval"],
    displayName: ["Elon Musk", "jack", "Naval"],
    followersCount: ["180000000", "6500000", "2000000"],
    verified: ["true", "false"],
    profileImageUrl: [
      "https://pbs.twimg.com/profile_images/.../photo.jpg",
    ],
  };
  return examples[field] ?? [];
}

function buildNewSchemaMap(
  current: SchemaMap,
  repairs: RepairResult[],
): SchemaMap {
  const newPaths = { ...current.paths };

  for (const repair of repairs) {
    const currentPaths = newPaths[repair.field as keyof typeof newPaths];
    if (Array.isArray(currentPaths)) {
      (newPaths as any)[repair.field] = [
        repair.newPath,
        ...currentPaths.filter((p: string) => p !== repair.newPath),
      ];
    }
  }

  return {
    version: current.version + 1,
    createdAt: new Date().toISOString(),
    createdBy: repairs.some((r) => r.method === "ai-inference")
      ? "ai-repair"
      : "deterministic-repair",
    changelog: repairs.map((r) =>
      `${r.field}: -> ${r.newPath} (${r.method})`
    ).join("; "),
    paths: newPaths,
  };
}

Usage in a Cloudflare Worker

export default {
  async fetch(request: Request, env: Env): Promise<Response> {
    if (request.method !== "POST") {
      return new Response("Method not allowed", { status: 405 });
    }

    const body = await request.json() as {
      endpoint: string;
      tweetResults: Record<string, unknown>[];
    };

    const parser = new SelfHealingParser({
      bucket: env.SCHEMA_MAPS_BUCKET,
      endpoint: body.endpoint,
      driftConfig: {
        driftThreshold: 0.0,
        repairCooldownMs: 600_000,
      },
    });

    const { tweets, driftReport, repairPlan } = await parser.parseBatch(
      body.tweetResults,
      {
        aiApiKey: env.OPENAI_API_KEY,
      },
    );

    return Response.json({
      tweets,
      meta: {
        total: tweets.length,
        schemaVersion: driftReport.schemaVersion,
        driftStatus: driftReport.status,
        repaired: repairPlan?.repairs.length ?? 0,
      },
    });
  },
};

interface Env {
  SCHEMA_MAPS_BUCKET: R2Bucket;
  OPENAI_API_KEY: string;
}

The Chrome Extension Side

Here’s how the Chrome extension intercepts GraphQL responses and forwards them:

// content-script.ts (injected in MAIN world)
// Intercepts X/Twitter's GraphQL responses

const TRACKED_ENDPOINTS = new Set([
  "HomeTimeline",
  "Bookmarks",
  "UserTweets",
  "ListLatestTweetsTimeline",
  "SearchTimeline",
]);

const originalFetch = window.fetch;

window.fetch = async function (...args: Parameters<typeof fetch>) {
  const response = await originalFetch.apply(this, args);

  try {
    const url = typeof args[0] === "string" ? args[0] : args[0]?.url;
    if (!url) return response;

    // Check if this is a GraphQL request we track
    const endpoint = TRACKED_ENDPOINTS.values().find((ep) =>
      url.includes(`/graphql/`) && url.includes(ep)
    );

    if (!endpoint) return response;

    // Clone the response so we don't consume the body
    const clone = response.clone();
    const json = await clone.json();

    // Extract tweet results from the response
    const tweetResults = extractTweetResults(json, endpoint);

    if (tweetResults.length > 0) {
      // Forward to our backend (fire and forget)
      navigator.sendBeacon(
        "https://your-worker.your-domain.workers.dev/parse",
        JSON.stringify({
          endpoint,
          tweetResults,
          capturedAt: new Date().toISOString(),
        }),
      );
    }
  } catch (err) {
    // Never break the original page
    console.debug("[self-healing-parser]", err);
  }

  return response;
};

/**
 * Extract individual tweet result objects from a GraphQL response.
 * This itself uses a simplified version of the self-healing pattern.
 */
function extractTweetResults(
  response: any,
  endpoint: string,
): Record<string, unknown>[] {
  const results: Record<string, unknown>[] = [];

  // Find instructions (the container for timeline entries)
  const instructions = findInstructions(response, endpoint);

  for (const instruction of instructions) {
    if (instruction.type !== "TimelineAddEntries") continue;

    for (const entry of instruction.entries ?? []) {
      // TimelineItem -> single tweet
      const itemContent = entry?.content?.itemContent;
      if (itemContent?.tweet_results?.result) {
        results.push(itemContent.tweet_results.result);
        continue;
      }

      // TimelineModule -> group of tweets (e.g., conversation threads)
      const items = entry?.content?.items;
      if (Array.isArray(items)) {
        for (const item of items) {
          const nested = item?.item?.itemContent?.tweet_results?.result;
          if (nested) results.push(nested);
        }
      }
    }
  }

  return results;
}

function findInstructions(response: any, endpoint: string): any[] {
  // Different endpoints nest instructions differently
  const knownPaths: Record<string, string> = {
    HomeTimeline: "data.home.home_timeline_urt.instructions",
    Bookmarks: "data.bookmark_timeline_v2.timeline.instructions",
    UserTweets: "data.user.result.timeline_v2.timeline.instructions",
    ListLatestTweetsTimeline: "data.list.tweets_timeline.timeline.instructions",
    SearchTimeline: "data.search_by_raw_query.search_timeline.timeline.instructions",
  };

  const path = knownPaths[endpoint];
  if (path) {
    const result = walkPath(response, path);
    if (Array.isArray(result)) return result;
  }

  // Fallback: search for instructions array
  const found = deepFind(response, "instructions", 6);
  return Array.isArray(found) ? found : [];
}

Example 1: Basic walkPath with Arrays

/**
 * Enhanced walkPath that handles array indices in dot-paths.
 * Supports paths like "entries.0.content.itemContent"
 */
function walkPathWithArrays(obj: unknown, path: string): unknown {
  const segments = path.split(".");
  let current: unknown = obj;

  for (const segment of segments) {
    if (current === null || current === undefined) return undefined;

    if (Array.isArray(current)) {
      const index = parseInt(segment, 10);
      if (isNaN(index)) return undefined;
      current = current[index];
    } else if (typeof current === "object") {
      current = (current as Record<string, unknown>)[segment];
    } else {
      return undefined;
    }
  }

  return current;
}

// Usage
const response = {
  data: { entries: [{ content: { text: "hello" } }, { content: { text: "world" } }] },
};

walkPathWithArrays(response, "data.entries.0.content.text"); // "hello"
walkPathWithArrays(response, "data.entries.1.content.text"); // "world"