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hindsight-architect

Expert memory architect. Understands your application, identifies where memory adds value, and produces an implementation plan with bank config, tag schema, and code.

Quality

68%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/hindsight-architect/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

The body is highly actionable with an executable detection preamble, complete code templates, and a well-sequenced interactive methodology with explicit validation gates. Its weaknesses are repetition (several key points restated multiple times), an off-task Enterprise Outreach section, and a monolithic single-file structure that inlines ~200 lines of product reference material that belongs in a separate file.

Suggestions

Move the "Your Expertise: Hindsight Product Knowledge" section (Retain/Recall/Reflect/Banks/Tags/Mental Models, ~200 lines) into a references/ file (e.g. references/hindsight-product.md), keeping a short summary plus one-level-deep links in SKILL.md.

Deduplicate recurring points — the "tags are identity scopes, not content classification" point appears three times and the mental-model retrieval-strategy point twice; state each once and cross-reference.

Trim or externalize the "Enterprise Outreach" section (~20 lines with a booking link); it does not advance the skill's core planning function and competes with the token budget.

DimensionReasoningScore

Conciseness

Most content is genuinely non-obvious product knowledge (retain/recall/reflect parameters, tags_match semantics, mental model design) that earns its place, but there is noticeable tightening potential: the "tags filter whose memories, not content classification" point is explained at least three times ("Tags" section, "Connecting Opportunities to Tags", "Challenge assumptions"), the mental-model-retrieval-strategy point is repeated nearly verbatim twice (lines ~285 and ~675), and the ~20-line "Enterprise Outreach" section with a Calendly link is off-task for the skill's core function. This matches anchor 3 ("includes some unnecessary explanation or could be tightened") rather than 2, since the padding is a minority of the document and there is no explaining of concepts Claude already knows.

3 / 5

Actionability

The body provides a fully executable 140-line bash preamble for environment detection, copy-paste-ready Python and Node.js templates for every operation (create_bank, retain, recall, create/get_mental_model, client setup), exact env-var blocks, install commands per deployment mode, and a concrete output format with a checklist. Placeholders like {tags} are explicitly template variables for the generated plan ("Replace {placeholders} with values from the design"), which is justified rather than pseudocode. This matches anchor 5 ("copy-paste ready code or commands; specific examples cover the common cases").

5 / 5

Workflow Clarity

The methodology is a clearly sequenced four-phase process with explicit checkpoints: preamble detection branches (configured vs. not, Path A/B/C), one-question-at-a-time AskUserQuestion gates, a defined gate before Phase 3 ("What You Need Before Moving On... Don't move to Phase 3 until you can make the three decisions"), a user confirmation loop ("Confirm: 'Does this design work?' Adjust if needed"), and a checklist in the generated plan. This matches anchor 5 (explicit validation steps, feedback loops, checklists); the skill is a planning skill so no destructive-operation cap applies.

5 / 5

Progressive Disclosure

The body is well-sectioned and navigable (not anchors 1-2's monolithic/buried-reference cases), but there are no bundle files at all — the ~200-line "Your Expertise: Hindsight Product Knowledge" section (Retain/Recall/Reflect parameter tables, mental model semantics) is exactly the API-reference-style content that belongs in a separate references/ file, inlined here in an ~855-line SKILL.md. This matches anchor 3's example ("[200 lines of API reference that could be in a separate file]" under organized headers); it is not 4 because no one-level-deep references exist and the main file carries all the detail.

3 / 5

Total

16

/

20

Passed

Description

46%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description answers "what" concretely but omits any "when to use" trigger guidance and uses second-person voice ("Understands your application"), which triggers a specificity penalty. It also leans on product jargon ("bank config", "tag schema") over natural user vocabulary, weakening trigger matching.

Suggestions

Add an explicit trigger clause naming natural user phrasing, e.g. "Use when the user wants to add persistent memory or a 'remember' capability to their application, mentions forgetting users between sessions, or asks how to retain and recall conversation history."

Rewrite in third person to remove the second-person "your application" (e.g. "Analyzes the user's application..."), which currently costs a specificity point under the voice rule.

Include natural synonyms and the product name — "Hindsight", "agent memory", "personalization", "user preferences" — instead of jargon-only terms like "bank config" and "tag schema".

DimensionReasoningScore

Specificity

The description lists several concrete actions and outputs — "Understands your application, identifies where memory adds value, and produces an implementation plan with bank config, tag schema, and code" — which matches the several-specific-actions anchors (4-5). However, "your application" is second person, triggering the mandated 1-point voice penalty from a baseline of 4. It also falls short of anchor 5's comprehensive coverage since actions like "understands" and "identifies" are process-generic rather than concrete deliverables.

3 / 5

Completeness

The "what" is clear — analyzes the application and produces an implementation plan with bank config, tag schema, and code — but there is no "Use when..." clause or equivalent trigger guidance anywhere, so per the judging guidelines completeness is capped at 3. It is not 2 because the "what" is concrete rather than vague, and not 4 because "when" is entirely absent, not merely under-specified.

3 / 5

Trigger Term Quality

Only one or two keywords a user would naturally say are present ("memory", "implementation plan"), while "bank config" and "tag schema" are product jargon users would not utter. Missing natural phrases like "remember", "forget", "persistent memory", "agent memory", "conversation history", or "personalization", and the product name "Hindsight" never appears. This sits noticeably below the midpoint, matching anchor 2 ("One or two generic keywords; missing the natural phrases users say") rather than anchor 3, which requires a broader set of relevant keywords with only synonyms missing.

2 / 5

Distinctiveness Conflict Risk

The memory-architect niche plus Hindsight-specific terms ("bank config", "tag schema") make it mostly distinct with only minor overlap risk against other memory-related skills. It is not 5 because "Expert memory architect" alone, without the product name or clearer niche markers, could still collide with generic memory/agent-memory skills.

4 / 5

Total

12

/

20

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (859 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

Repository

vectorize-io/hindsight

Path

skills/hindsight-architect/SKILL.md

Reviewed

Table of Contents

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