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AF agent-harness-engineering

Bootstrap or upgrade a software repository for agent-first engineering. Use when a user wants to improve project-wide development discipline around `AGENTS.md`, progressive-disclosure docs, agent-readable architecture/context, mechanical quality checks, CI-enforced structure, or optional garbage-collection/maintenance loops.

ClawHub Agent Skills author: yeyitech v0.1.0 8 files body ≈ 1 376 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process F 50/100 · Will not run — References files that are not bundled: scripts/agent_repo_check.py, scripts/agent_gc_report.py

ProcedureAI and agentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
F
50/100
Will not run
References files that are not bundled: scripts/agent_repo_check.py, scripts/agent_gc_report.py
Tools and files w 18
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

  1. The text references files that are not there: add them or drop the references.
For the model run — optional
  • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
  • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

Guard findings · 0

✓ No critical or high findings

Files scanned: 8. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning missing-ref reference to a missing file: scripts/agent_repo_check.py
  • warning missing-ref reference to a missing file: scripts/agent_gc_report.py

Process rating: all ten parameters 50/100

Will not run. References files that are not bundled: scripts/agent_repo_check.py, scripts/agent_gc_report.py
  • 0Tools and files. 2 referenced file(s) missing: scripts/agent_repo_check.py, scripts/agent_gc_report.py
  • 0Inputs and preconditions. Does not say what the process needs to start
  • 0Progress reporting. Says nothing while it works
  • 30Running it twice. 4 mutating operations with no state check
  • 40Result and completion. Does not say what the result is
  • 55Failures and branches. 1 branches
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 74 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 1376 tokens

Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

Quality signals

  • +5Description has no quoted example phrases that should trigger the skill
  • +4Description does not say when NOT to use the skill (false activations)
  • +3Output format is not stated: the model decides each time
  • -5TODO / placeholder text left in the skill
  • +1No license
  • +2Single-language instructions
  • +3Description length 326: enough signal without eating the budget
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 74 items
  • +4Has examples (1 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)
  • +3All 1 scripts are documented

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.

External checks

ClawHub: clean
This is a disclosed repository-scaffolding skill that makes persistent project documentation and check-script changes, with no evidence of credential access, exfiltration, or destructive behavior.
LLM: benign (medium) · VirusTotal: · 29 May 2026