Hermes loop engineering
Skill Gesondian/ai-collab-governance-skills/skills/hermes-loop-engineering
Hermes Loop Engineering skills for self-improving AI delivery governance.
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Use when repeated delivery failures, disputed acceptance, weak evidence patterns, owner-routing mistakes, or trial observations may need reusable governance improvement.
SKILL.md
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Hermes Loop Engineering
Core Principle
Turn delivery failures into reusable governance improvements only after evidence, observation, and forward-testing support the change.
This is human-in-the-loop self-improvement, not automatic model training.
When To Use
Use this skill after an intake, evidence-boundary, acceptance-scope, or owner-boundary review surfaces a pattern that may recur.
Typical triggers:
- the same kind of UAT reopen appears again,
- an agent repeats an evidence overclaim,
- a verifier repeatedly accepts beyond scope,
- owner routing fails for the same reason,
- a candidate rule may need to become wording, a template field, or a hard gate,
- a trial observation needs a keep / revise / promote / drop decision.
Loop
- Capture the delivery failure or disputed outcome.
- State the evidence boundary.
- Identify the smallest failed gate or missing evidence.
- Decide whether the signal is one-off, repeated, or systemic.
- Fill or reference a
trial_observation. - Choose the smallest improvement:
- keep as observation,
- revise wording,
- add or revise a template field,
- add an example,
- forward-test a candidate,
- consider a hard gate.
- Record false-positive, false-negative, and maintenance risks.
- Return keep / revise / promote / drop.
Output Contract
verdict:
failure_signal:
evidence_boundary:
candidate_pattern:
reuse_scope:
not_reuse_scope:
observed_repetition:
agent_rationalization:
recommended_change:
forward_test_needed:
forward_test_result:
behavior_change_evidence:
promotion_level:
false_positive_risk:
maintenance_cost:
rollback_condition:
next_owner:
must_not_claim:
Verdict Vocabulary
| Verdict | Use When |
|---|---|
record_observation | The signal is useful but not yet reusable. |
revise_wording | A light wording change can reduce a repeated mistake. |
revise_template | A missing field or template shape causes repeated gaps. |
add_example | A concrete scenario would teach the judgment better than a rule. |
forward_test_candidate | The candidate looks useful but needs pressure testing. |
promote_to_hard_gate_candidate | The pattern is repeated, high-risk, low-noise, and cheap to check. |
drop_candidate | The candidate is too broad, noisy, stale, or misleading. |
Self-Improvement Proof
Do not treat a skill or template edit as proof that the system improved.
Minimum proof:
- a prior failure is captured,
- the old agent behavior or bad conclusion is visible,
- an artifact changes,
- a forward-test or later real case shows a better decision,
- false-positive and maintenance cost stay acceptable.
If behavior change is not observed, return forward_test_candidate, not promote_to_hard_gate_candidate.
Common Mistakes
- Treating one failure as proof that a new rule is needed.
- Calling a document edit "self-evolution" without testing whether it changes agent behavior.
- Promoting a warning into a hard gate before measuring false positives.
- Adding governance weight when a narrower example or wording change would work.
- Keeping stale candidates because they sound important.
Safety Boundary
Do not claim the system has learned unless a later artifact, example, template, or skill changes future agent behavior in a bounded way.
Prefer the smallest durable improvement that reduces the observed mistake.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.