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fix-failed-paperclip-run

Investigate failed, stalled, slow, or incorrectly blocked Paperclip tasks. Trace the cause, reproduce the bug, implement a repair, test related behavior, and verify the actual workflow before delivering a pull request with passing checks.

OriginalShow the original description

Diagnose and fix failed, stuck, slow, or incorrectly blocked Paperclip tasks and runs. Use with a task/run URL or failure report to trace the root cause, prove a regression red then green, check second-order effects, verify the real workflow, and deliver a PR with passing checks.

AI rating90/ 100See how to install

Good for

  • Repairing a failed Paperclip task or run
  • Investigating stalled or incorrectly blocked work
  • Measuring and fixing slow task execution
  • Verifying a repair through the actual user workflow

Overview

What it does

Investigates and repairs Paperclip tasks or runs that fail, stall, run slowly, or are incorrectly blocked. Starting from a task URL, run URL, or failure report, it compares logs, settings, and saved state. It prepares a test that reproduces the failure before making a repair, then checks success and effects on related behavior. The result is a pull request that passes the required checks and review.

When it helps

Use it when a run reports success but its output never arrives, or when repeated retries fail to move work forward. It distinguishes code defects, configuration problems, and fixes that have not reached the working environment, choosing a response based on the evidence. It also checks saved outputs and completion state through the actual user workflow.

Example requests

  • “Investigate this failed run URL and create a reproduction test and repair PR.”
  • “Find why this task stalled, then verify the repair using the actual workflow.”

Notes

Requires authorized access and credentials appropriate to the environment. If live verification is unavailable, it reports that limitation. The skill does not authorize merging or deployment. Recovery of the original task depends on the granted authority and requested scope.

This overview was written by AI (OpenAI Codex) from the public SKILL.md. Check the original below for the exact behavior.

Objective evaluation

AI rated this skill on four points using only the contents of its SKILL.md. Stars and popularity are not taken into account.

Clear instructionsIs it clear when to use it and what it does?
AGoodIt defines the scope from investigating failures, stalls, and latency to verifying the user workflow and delivering a PR.
Concrete stepsAre steps, inputs, outputs, and examples spelled out?
AGoodIt specifies log correlation, failure reproduction before repair, verification afterward, and tests for secondary effects.
SafetyAre sending data, deleting files, and running commands disclosed?
AGoodIt requires redacting credentials, respecting access boundaries, preserving work before retries, and limits merge authority.
Easy setupDoes it need few tools or API keys, and are they documented?
BPartly lackingIt requires checking the runtime environment and credential source, but provides no installation or authentication setup procedure.

The total counts each point as A = 25, B = 15, C = 5. Rated by AI (OpenAI Codex) as of Oct 20, 2026. Treat it as a guide and read the original before installing.

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Files included (2)

  • SKILL.md12.9 KB
  • references/failure-patterns.md7.5 KB

SKILL.md (original)

Review the instructions the agent will receive before you install.

Fix Failed Paperclip Run

Own the failure from evidence through a tested repair and a green PR. A diagnosis, a proposed plan, a successful provider turn, and a passing unit test are intermediate results. The finish line is a verified user workflow and a PR whose current commit passes the required checks and review.

Honor an explicit diagnosis-only request or a calling workflow's approval boundary. Otherwise proceed through implementation without a separate plan-approval pause. Carry forward existing authorization for merge, rollout, and recovery; do not ask again. When the assignment ends at a PR, distinguish local verification from deployment and recovery of the original task. This skill does not itself authorize merging or deploying.

1. Establish the incident and the finish line

Start from the supplied task/run URL or failure report. Resolve these facts from available tools and records before asking the user for missing information:

  • Instance, company, task, affected run(s), time window, and intended useful result.
  • Current state versus state at failure; parent/child tasks and pending interactions only where they control progress. Multiple failed runs can have different causes.
  • The code actually served, runner/provider pack versions, operating system and architecture, adapter, model/effort, execution workspace, trust mode, and credential source. Trace effective values back through task overrides, inherited settings, defaults, and saved session state. Redact credential values.
  • Which endpoint is authorized: verified fix and green PR, or also rollout and recovery. Preserve the requested work and success criteria throughout.

Use authorized diagnostic access. If company scoping blocks a read, use an already-authorized diagnostic path or label the missing evidence; do not bypass the boundary.

Read repository instructions and the relevant current execution contract. In this repository, use doc/execution-semantics.md for ownership, continuation, waiting, finalization, and recovery. Inspect recent changes and open/merged repairs in the affected area. A correct fix that has not reached the target is a deployment gap; do not create a second patch without evidence that the first is insufficient. Choose code repair, configuration repair, or recovery from that evidence. If an existing fix is sufficient, verify it and continue to the authorized endpoint using its PR; do not manufacture code changes for an already-fixed defect.

Keep a compact evidence record in the working notes or driving issue: environment, revision, causal timeline, reproduction, safety assumptions, and acceptance result. Preserve useful logs and saved work before retries or cleanup. Do not put secrets or private incident links in public code, fixtures, skills, or PR descriptions.

2. Find the first causal failure

Reconstruct the run from dispatch through preparation, provider execution, tool calls, result persistence, finalization, and task disposition. For chat/email work, follow outbound delivery and the user-visible result too. Align run events, logs, controller/provider records, task activity, and traces by time and identity.

Separate these questions:

  1. What first prevented useful work or its delivery?
  2. Why did the responsible component make that decision or emit that value?
  3. Why did recovery or continuation fail to complete the already-authorized work?
  4. Which later errors are consequences, misleading summaries, or independent bugs?

Trace the causal value or transition to its producer and shared contract. Verify the hypothesis against a counterexample or a reproduction. Check logs before and after the apparent error: a recovered authorization warning may not be fatal, and “final response,” HTTP 200, or “run succeeded” may not mean the task is complete. Missing error detail is an evidence gap, not proof of a provider outage. When evidence cannot distinguish hypotheses, name the uncertainty and add the smallest bounded, redacted observation or fault injection that will resolve it.

For a stalled tree, classify each relevant non-progressing node as genuinely waiting on a named human/external dependency, agent-actionable but unrouted, or already covered by existing work. Preserve all three invariants:

  • Productive work has a real next action and owner.
  • Genuine approval, credential, budget, and dependency blockers remain effective.
  • Recovery and continuation are bounded and cannot recursively recover themselves.

Consult failure patterns for matching mechanisms and test oracles. Treat them as hypotheses to check, not a catalog of explanations.

3. Prove the bug before changing production code

Write the smallest behavioral regression at the boundary that owns the defect. Run it against the unfixed code and capture the expected failure. A setup error, missing dependency, or assertion about a mock's own return value is not the red.

  • Use the real policy/resolver/serializer and realistic persisted input. Mock only boundaries outside the behavior under test. If a query duplicates rows, exercise that query with the relational shape that produces duplicates.
  • For recovery or races, control the interruption point and assert the resulting ownership, command receipts, side effects, and continuation. Restarting between completed turns does not reproduce a restart during an in-flight side effect.
  • For packaging, process startup, platform, or cross-language defects, reproduce using the actual affected artifact or protocol. A host binary or mocked provider cannot prove that a sandbox image contains an executable compatible binary.
  • For latency, measure the user's full interval and the suspected stage under the relevant cold/warm conditions. Use a stable controlled regression where possible; keep noisy wall-clock comparisons as measured acceptance evidence.

Keep this failing regression as the starting point for the repair. If the defect requires a real integration environment, capture a repeatable failing integration probe before the fix and repeat it afterward; add a durable regression at the closest reliable boundary. If no meaningful red can be observed, report that gap and keep working on reproduction. Do not claim red-green proof from a test that was first run after the fix.

4. Repair the cause and prove the safety assumptions

Fix the responsible producer or shared transition at the narrowest correct scope. Check sibling callers/adapters that share that contract; generalize only as far as the evidence requires. Avoid historical string allowlists, swallowed errors, unbounded retries, or relaxing an integrity check to accommodate malformed input. Preserve the requested model, trust, isolation, and performance behavior unless a change or explicit fallback policy is part of the assignment.

Before implementation, state the behavioral rule and the strongest plausible second-order failure. Trace callers, downstream consumers, persisted state, and runtime variants affected by the changed contract. Turn the important safety assumptions into observable regression cases, not just assurances in the PR:

Changed boundarySafety evidence to consider
Ownership, cancellation, retry, or continuationNo second executor or repeated external side effect; legitimate queued work still runs; cancellation remains effective. An unchanged assignee field alone is insufficient.
Recovery or finalizationSaved files, results, and sessions survive; known-complete work is not rerun; interrupted work continues once; indeterminate outcomes reconcile before retry.
Waiting or task dispositionReal approval/budget/credential gates still stop work; actionable work has a durable wake; recovery is bounded; no recursive recovery tree.
Input, tool, or credential plumbingCompany and trust boundaries hold; intentional configuration reaches execution without ambient secret forwarding; distinct tools still dispatch correctly; sensitive logging remains redacted.
Packaging, compatibility, or session identityRelevant local/sandbox, platform, fresh/resumed, and old/new artifact combinations work; obsolete saved values are handled by a contract rather than a list of incident exceptions.
Performance or lifecycleWarm reuse remains warm where intended; queueing and finalization do not regress; configured deadlines retain their meaning.

Select cases that exercise the actual blast radius. Do not create an unrelated platform matrix. Test the sibling success path and the negative case that the old guard protected. Keep reproduction and safety tests through later revisions.

With the red established and the safety assumptions identified, make the repair and run the same regression green. Run the relevant safety cases and existing tests. Refactor only while they stay green. Investigate unexpected failures instead of weakening an assertion or deleting the regression to finish.

Routine infrastructure facts belong to the harness when it can determine them under existing authority. Reconcile process ownership, saved results, or eligible continuations in code instead of asking a user to certify those facts in a form. Keep UI choices for genuine user intent, authorization, or unavailable information.

5. Verify the real workflow at the fixed revision

Apply the repository's verify-paperclip skill and affected map recipes when available. Its essential requirement here is a real run through the affected user entry point, using the fixed source or package and the relevant runtime configuration. Run the narrow regression first so each hypothesis gets fast feedback; use a local server with the real remote provider when that reproduces the failure. Do not wait for a full deployment for every experiment when a faithful shorter loop exists.

Exercise the original failure mechanism with a representative task: actual adapter, provider, tools, trust boundary, persisted state, artifact, and interruption point where each matters. A mock, Storybook, provider-free benchmark, or generic “hello” smoke is supporting evidence only when the incident depends on a different path.

Verify the useful output, persistence after reload/reconnect, finalization, and correct task state with no stranded lock or pending operation. For resume and conversation defects, run a second turn and check retained context/files. For delivery defects, check the destination and user-visible outcome, not just receipt of an HTTP request. For slowness, compare the end-to-end interval with the baseline.

Record the tested revision and runtime versions. Re-run affected acceptance after changes that invalidate that evidence. Report any missing credentials, access, or environment precisely; an unavailable live check remains unverified. Continue independent repair and PR work instead of substituting an unsupported “works” claim.

6. Deliver the PR and close the incident to the authorized endpoint

Use prepare-paperclip-pr and prcheckloop when available. Follow repository instructions for checks, branch isolation, PR template, and review. Open the PR, address review and CI failures, and inspect required checks and review on the current head SHA. Pending, skipped, and passing are distinct. Retry a failed check only with a reason; retain its evidence and fix repeatable failures. Resolve companion repository/artifact changes needed by the fix and state their ordering.

The PR must explain the causal defect, behavioral change, failing-before/passing- after evidence, safety regressions, actual acceptance environment, and limitations. Do not declare it ready while required checks or review remain unresolved. If a required service or permission prevents completion, name the exact remaining gate.

When rollout/recovery is already authorized, continue: confirm the served revision and required artifact versions, re-read the original task, and recover it through the normal product path. Reconcile existing work before starting another executor. Observe the original useful result or a genuine, named remaining blocker. Do not cancel, replace, or shrink the user's original task merely to make a test pass.

Finish with a concise report:

  • Root cause and what changed, with unresolved hypotheses labeled.
  • Red/green regression and second-order behavior checked.
  • Real workflow exercised, revision/environment, and observed result.
  • PR URL, current check/review status, and companion changes if any.
  • Whether the original task is recovered, still awaiting rollout, or blocked on a specific remaining dependency. Separate code readiness from incident recovery.

Do not stop at a plan when a fix is authorized. Do not call the incident resolved because its error message improved or a successful run badge appeared.

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