Agent skill

Kayba Stage 7 Fixer

by kayba-ai in kayba-ai/agentic-context-engine

Implement the approved fixes from the action plan and log all changes.

Apache-2.0Auto-check passedAgent Workflows

Install Kayba Stage 7 Fixer

skills CLI
$ npx skills add kayba-ai/agentic-context-engine --skill kayba-stage-7-fixer -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install kayba-ai/agentic-context-engine kayba-stage-7-fixer --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/kayba-ai/agentic-context-engine.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.claude/skills/kayba-pipeline/stage-7-fixer .claude/skills/kayba-stage-7-fixer && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
kayba-stage-7-fixer
GitHub stars
2.6k
Token cost
~1.8k tokens
SKILL.md length
790 words
Files
1
Skills in repo
8
Repo updated
First seen
Licence
Apache-2.0

At a glance

Implement the approved fixes from the action plan and log all changes.

  • The user says run stage 7
  • SKILL.md covers Inputs, Pre-flight: Git Safety…, Pre-flight: HITL Modification… and Pre-flight: Conflict Scan, plus 5 more sections
  • Calls git and python
  • Implement fixes

What it does

Kayba Stage 7 Fixer is an agent skill from kayba-ai/agentic-context-engine. Implement the approved fixes from the action plan and log all changes. Trigger when the user says "run stage 7", "implement fixes", "apply action plan", or when invoked by the kayba-pipeline orchestrator. Requires eval/actionplan.md to exist.

Its SKILL.md is about 1.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Agent Workflows. It works with Git. The repository describes itself as: 🧠 Make your agents learn from experience. Now available as a hosted solution at kayba.ai. The licence is Apache-2.0.

When your agent uses it

  • The user says run stage 7
  • Implement fixes
  • Apply action plan
  • Invoked by the kayba-pipeline orchestrator

Example prompts

  • “run stage 7”
  • “implement fixes”
  • “apply action plan”
  • “/kayba-stage-7-fixer”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit 3a31983. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • git
    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use git, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Kayba Stage 7 Fixer loads about 1.8k tokens when it runs. Until then it costs about 66 tokens; SKILL.md has 790 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~66
When it runs · the whole SKILL.md, loaded when a task matches
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from kayba-ai/agentic-context-engine at commit 3a31983, republished under its Apache-2.0 licence (© kayba-ai). 790 words, ~1,846 tokens.

Download SKILL.mdSave it as .claude/skills/kayba-stage-7-fixer/SKILL.md (or your agent's skills folder).
name
kayba-stage-7-fixer
description
Implement the approved fixes from the action plan and log all changes. Trigger when the user says "run stage 7", "implement fixes", "apply action plan", or when invoked by the kayba-pipeline orchestrator. Requires eval/action_plan.md to exist.

Stage 7: Fix Implementation

Implement every non-discarded fix from the approved action plan.

Inputs

  • eval/action_plan.md -- the approved action plan from Stage 5 (possibly modified during HITL in Stage 6)
  • eval/stage6_decision.md -- if it exists, the HITL decision record from Stage 6 (contains user modifications)
  • eval/baseline_metrics.json -- the pre-fix baseline metrics from Stage 3 (for reference in changes log)

Read the action plan and stage6 decision (if present) before starting.

Pre-flight: Git Safety Checkpoint

Before making ANY changes to source files:

  1. Run git status to confirm the working tree state
  2. Create a safety commit or stash:
    git stash push -m "pre-pipeline-fixes-$(date +%Y%m%d-%H%M%S)"
    If there are no uncommitted changes to stash, create a lightweight tag instead:
    git tag pre-pipeline-fixes-$(date +%Y%m%d-%H%M%S)
  3. Record the stash ref or tag name in eval/changes_log.md under a "Rollback" section so the user can restore if needed

This ensures every fix is reversible with a single git stash pop or git checkout.

Pre-flight: HITL Modification Check

If eval/stage6_decision.md exists:

  1. Read it and identify any items the user modified, added, or re-prioritized during Stage 6
  2. Build a set of HITL_MODIFIED_IDS -- the insight/skill IDs that the user changed
  3. When logging each fix later, tag modified items with [HITL-MODIFIED] in the changes log so reviewers know which fixes reflect user judgment vs. the original pipeline output

If the file does not exist, assume no HITL modifications were made.

Pre-flight: Conflict Scan

Before implementing any fixes, scan the action plan for potential conflicts:

  1. Build a map of file_path -> [fix IDs that touch it]
  2. If two or more fixes modify the same file, flag them as co-located
  3. If two or more fixes modify the same section (within ~20 lines of each other), flag them as overlapping
  4. For overlapping fixes: plan to apply them sequentially in priority order, re-reading the file between each edit to ensure the second fix still makes sense on top of the first
  5. Log any detected conflicts at the top of eval/changes_log.md under a "Conflict Notes" section

Process

Work through the action plan in priority order. For each non-discarded fix:

1. Understand the fix
  • Read the recommendation carefully
  • Read the referenced files in the codebase
  • Understand the surrounding code before making changes
  • Check if this fix was flagged as co-located or overlapping in the conflict scan. If overlapping with a previously-applied fix, re-read the target file to see the current state after prior edits
2. Implement the change

For code fixes:

  • Find the relevant files
  • Make the minimal, targeted change described in the recommendation
  • Do not refactor surrounding code unless the fix obviously breaks without light adjacent cleanup (e.g., an import is missing, a variable was renamed). If you make adjacent cleanup, log it explicitly as "adjacent cleanup" in the change entry
  • Do not add features beyond what was recommended

For prompt fixes:

  • Find the system prompt file (use domain context from Stage 2 if needed)
  • Add the recommended instruction at the appropriate location
  • Do not rewrite existing prompt text unless the recommendation explicitly says to
Show full SKILL.md (303 more words)Show less
3. Log the change

Append to eval/changes_log.md:

markdown
## Fix N: [skill/insight name] [HITL-MODIFIED if applicable]
**Type:** code fix | prompt fix
**Verdict from action plan:** [quote the recommendation]
**Files modified:**
- `path/to/file.py` -- [what changed and why]
**Before:**
\```
[relevant snippet before change]
\```
**After:**
\```
[relevant snippet after change]
\```
**Linked metrics:** [which metrics this should improve]
**Conflict notes:** [if this fix overlapped with another, note it here; otherwise "none"]
4. Handle uncertainty (NEEDS REVIEW workflow)

If a fix requires changes you are unsure about:

  1. Do NOT implement it
  2. Log it as NEEDS REVIEW in the changes log with:
    • What specifically is unclear
    • What information would resolve the ambiguity
    • The files and lines you examined
  3. Continue to the next fix -- do not block the pipeline
  4. At the end of all fixes, collect all NEEDS REVIEW items into a dedicated section (see Output format below). The pipeline does NOT stop; these items are presented to the user after all other fixes are applied.

Post-Fix: Next Steps (Do NOT Re-run Baselines)

Do NOT re-run compute_baselines.py as part of this stage. The baseline metrics were computed against the original traces, which reflect old agent behavior. Re-running against the same traces will show zero movement for prompt-only fixes and is misleading.

Instead, after all fixes are applied, include a Next Steps section in the changes log that tells the user:

  1. Generate new traces by running the agent with the updated prompts/code
  2. Then re-run baselines against the new traces:
    bash
    python eval/compute_baselines.py --traces-dir <new_traces_folder> --output eval/post_fix_metrics.json
  3. Compare eval/post_fix_metrics.json against eval/baseline_metrics.json to measure actual improvement

Rules

  • Do NOT modify trace files
  • Do NOT make changes beyond what the action plan recommends (except adjacent cleanup logged explicitly)
  • Make minimal, targeted changes -- don't clean up or refactor surrounding code
  • If the action plan says "discard", skip that entry entirely
  • You MAY write eval/changes_log.md as the primary output
  • Do NOT run eval/compute_baselines.py -- baselines should only be re-computed after new traces are generated with the updated agent

Output format

Write eval/changes_log.md:

markdown
# Changes Log

## Rollback
- **Safety ref:** `git stash` ref or tag name
- **To undo all fixes:** `git stash pop` or `git checkout <tag>`

## Conflict Notes
- [any file/region conflicts detected, or "No conflicts detected"]

## Summary
- Code fixes applied: N
- Prompt fixes applied: M
- Skipped / needs review: K
- HITL-modified items: J

---

## Fix 1: [skill name]
...

## Fix 2: [skill name]
...

---

## Needs Review
[Collected list of all NEEDS REVIEW items with context, or "None -- all fixes applied successfully"]

For each NEEDS REVIEW item:
- **Fix N: [skill name]**
- **What is unclear:** [specific ambiguity]
- **What would resolve it:** [information needed]
- **Files examined:** [paths and lines]

---

## Next Steps

To measure actual improvement:
1. Generate new traces by running the agent with the updated prompts/code
2. Re-run baselines:
\```bash
python eval/compute_baselines.py --traces-dir <new_traces_folder> --output eval/post_fix_metrics.json
\```
3. Compare `eval/post_fix_metrics.json` against `eval/baseline_metrics.json` to measure metric deltas

Outputs

  • eval/changes_log.md -- full log of all changes, conflicts, NEEDS REVIEW items, and next steps
  • The actual code/prompt changes in the repository
  • A git stash or tag for rollback

© kayba-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .claude/skills/kayba-pipeline/stage-7-fixer of kayba-ai/agentic-context-engine.

Open the folder on GitHubat commit 3a31983

Compare with similar skills

Kayba Stage 7 Fixer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Kayba Stage 7 Fixer compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Kayba Stage 7 Fixer this skillkayba-ai/agentic-context-engine2.6k—~1.8kAutomated safety check: PassApache-2.0
Darwin Skill Optimizeralchaincyf/darwin-skill6.2k1 repos~4.7kAutomated safety check: PassMIT
CodeGraph Agent Evalcolbymchenry/codegraph74k—~950Automated safety check: PassMIT
Neat-Freak Knowledge CloseoutKKKKhazix/khazix-skills21k—~1.9kAutomated safety check: PassMIT
O2 Review Loopopenobserve/openobserve22k—~3.7kAutomated safety check: PassAGPL-3.0
Beads Task Memorygastownhall/beads28k—~1.2kAutomated safety check: PassMIT

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Works with

Categories

Questions about Kayba Stage 7 Fixer

What does Kayba Stage 7 Fixer do?

Implement the approved fixes from the action plan and log all changes. Kayba Stage 7 Fixer is an agent skill from kayba-ai/agentic-context-engine. Implement the approved fixes from the action plan and log all changes.

When should I use Kayba Stage 7 Fixer?

Kayba Stage 7 Fixer fits situations like: the user says run stage 7; implement fixes; apply action plan; invoked by the kayba-pipeline orchestrator.

How do I install Kayba Stage 7 Fixer in Claude Code?

Run `npx skills add kayba-ai/agentic-context-engine --skill kayba-stage-7-fixer -a claude-code`. Or copy the skill folder (.claude/skills/kayba-pipeline/stage-7-fixer in kayba-ai/agentic-context-engine) into .claude/skills/kayba-stage-7-fixer in your project. Claude Code loads it when a task matches its description.

How do I install Kayba Stage 7 Fixer in Codex?

Run `npx skills add kayba-ai/agentic-context-engine --skill kayba-stage-7-fixer -a codex`. Or copy the skill folder (.claude/skills/kayba-pipeline/stage-7-fixer in kayba-ai/agentic-context-engine) into .agents/skills/kayba-stage-7-fixer in your project. Codex loads it when a task matches its description.

Can I use Kayba Stage 7 Fixer in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add kayba-ai/agentic-context-engine --skill kayba-stage-7-fixer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/kayba-stage-7-fixer, .gemini/skills/kayba-stage-7-fixer, .github/skills/kayba-stage-7-fixer and .opencode/skills/kayba-stage-7-fixer in your project.

What does Kayba Stage 7 Fixer need to run?

Going by SKILL.md and its folder, Kayba Stage 7 Fixer needs the command-line tools its instructions call (git and python). Our summary lists: Python 3.

Does Kayba Stage 7 Fixer access the network?

SKILL.md contains no URLs. Its commands use git, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Kayba Stage 7 Fixer safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Kayba Stage 7 Fixer use?

Kayba Stage 7 Fixer is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Kayba Stage 7 Fixer use?

About 1.8k tokens (SKILL.md is roughly 7.4k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Kayba Stage 7 Fixer?

Skills that share tags, products or a category with Kayba Stage 7 Fixer: Darwin Skill Optimizer (alchaincyf/darwin-skill, 6.2k stars), CodeGraph Agent Eval (colbymchenry/codegraph, 74k stars), Neat-Freak Knowledge Closeout (KKKKhazix/khazix-skills, 21k stars) and O2 Review Loop (openobserve/openobserve, 22k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Kayba Stage 7 Fixer?

kayba-ai (a GitHub organization) maintains it in kayba-ai/agentic-context-engine, which has 2,590 GitHub stars. The repository holds 8 skills in this directory. The repository was last updated on September 24, 2026.

Source: kayba-ai/agentic-context-engine on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.